As described in the cross-referenced PCT application, EEG measurements from auditory evoked responses (AER) or visual evoked responses (VER) detect voltage potentials from the brain as the brain processes the specific stimulus or sequence of stimuli. The pattern of EEG signals evoked in this way are called Evoked Response Potentials (ERPs). Specific combinations, sequences and timing of various stimuli which evoke an identifiable EEG response based on known neural processes are called ERP paradigms. Various ERP paradigms are used to evoke EEG responses that can be correlated with neurological disorders. ERPs from dyslexic children often show abnormally high peak voltages and long signal latencies. These characteristics may correlate to higher than normal energy requirements to process sounds and slower discrimination and sound-to-symbol mapping, the outward manifestations of which will primarily be difficulty in reading and writing.
With regard to these broader applications, research has increasingly sought greater insight into brainwaves, such as overviewed in “LINKING BRAINWAVES TO THE BRAIN: AN ERP PRIMER” by Alexandra P. Fonaryova Key, Guy O. Dove, and Mandy J. Maguire. Over the latter portion of the past century recordings of brain electrical activity such as the continuous electroencephalogram (EEG) and the stimulus- or task-relevant event-related potentials became frequent tools of choice for investigating the brain's role in cognitive processing and neurological disorders. Electrophysiological recording techniques are generally non-invasive, relatively inexpensive, and do not require that participants provide a motor or verbal response. Furthermore, virtually identical procedures can be used across the entire life span. While the ongoing EEG reflects a wide-range of neural activity related to the various sensory and cognitive functions, it is also affected by the myriad of self-regulation processes (e.g., maintaining temperature, heart rate, breathing) ongoing in the brain at the same time. This intermixing of signals makes it difficult to separate cognitive and physiological contributors to the observed EEG. By contrast, the ERP approach permits investigators to more directly link stimulus events to the recorded signal. The ERP is a portion of the EEG that is time-locked to the precise onset (or in some cases offset) of the stimulus presentation (e.g., sound or visual stimulus). Analyses generally focus on the change in the electrophysiological signal that immediately follows the stimulus event enabling researchers to evaluate the relationship between the neuroelectrical response and the stimulus. The ERP signal that is finally detected at the scalp is not an exact and completely stable pattern that reflects only those discrete neural events directly related to the evoking stimulus, the task, or the subject's state. The smaller size of the ERP relative to other physiological events can also make it difficult to discern the relevant signal. To accommodate these factors, researchers employ repeated presentations of the evoking stimulus to average out potentially unrelated events (those signals which are not time-locked to the stimulus).
ERPs have been successfully used to study both general and specific aspects of an individual's response to events in the external as well as internal environment. Neuropsychological research of cognitive functioning in various populations also demonstrated that ERP components could serve as informative markers of neurodevelopmental status in general as well as the development of more specific abilities. Additional advantages of the ERP technique over other neuroimaging procedures include (1) very fine temporal resolution (on the order of milliseconds or even fractions of a millisecond) that reveals even momentary changes in patterns of brain activation that otherwise could go unnoticed, and (2) relatively gross level spatial resolution capabilities that permit a basis for theorizing about the distribution of brain mechanisms that subserve these cognitive functions.
The ERP is generally believed to reflect post-synaptic (dendritic) potentials of a fairly extensive set of neurons activated in close temporal proximity. However, information recorded at the scalp cannot capture all of the generated electrical activity. Signals that originate within the brain must travel through a variety of tissues of different densities and resistances (e.g., neurons, fiber tracts, cerebral spinal fluid, skull) before reaching the recording electrode on the scalp. It may be difficult to detect a signal if the distance from the cortical or subcortical regions generating the signal to the scalp is too great relative to the signal's strength. In addition, the orientation of the cortical columns generating the signal may affect whether a signal reaches the scalp. If the columns are perpendicular to the scalp and signal strength is sufficient, the likelihood of the electrode detecting the signal is good. On the other hand, if the cell columns are oriented parallel to the scalp or at some other angle to it, the signal may project to an area away from the nearest electrode above it or not project to the scalp at all.
ERP waveforms are often described in terms of positive and negative peaks (i.e., the most positive and negative deflections in the wave). At a general level, the labeling can refer to the sequence in which the peak occurs while at the same time indicating its polarity. For example, “N1” would refer to the first negative going peak in the waveform while “N2” would refer to the second negative occurring peak. Likewise, “P1” refers to the first positive deflection or peak in the ERP waveform while “P2” refers to the second positive peak. The naming scheme for ERP components can also identify the positive and negative peaks by their latency (usually defined as the time from stimulus onset). “N100” in this example refers to the negative peak that occurs 100 ms following stimulus onset. “P300” would identify the positive peak that occurred 300 ms post stimulus onset.
Recently, it has been recognized that a comprehensive evaluation of the EEG producing mechanisms within the brain would have significant advantages, specifically the four ERP characteristics: peak latency, cognitive functional significance, cortical distributions, and component brain sources. For the purpose of consistency and clarity, the peaks are generally identified by their polarity (which itself can vary as a function of stimulus modality and reference location) and place in the sequence of components rather than by exact latency due to possible variations in the latter due to developmental, environmental, or clinical effects (unless the latency is the predominant descriptor of the peak).
Challenges exist for a comprehensive approach to detection and analysis due to generally-known techniques for source localization that rely on different principles which can produce conflicting results. Thus, findings from intracranial recordings, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), brain electromagnetic source analysis (BESA), positron emission tomography (PET), or low-resolution brain electromagnetic tomography (LORETA) may not always agree.
In addition, there are a large number of ERP peaks to detect and analyze characterized by peak amplitude and latency effects, scalp distributions and neural substrates. These include P1, N1, P2, N2, Mismatch negativity (MMN), P3a, P3b, N400, and P600. This list is not assumed to be exhaustive. Other ERP components such as the Contingent Negative Variation, the Left Anterior Negativity, the Late Positive Potential, and the Positive Slow Wave are not included in the current review due to a sparcity of information regarding their sources and/or the limited space available to cover a large amount of research.
The P1 peak is not always easily identified, but when present, occurs approximately 50 ms after an auditory stimulus onset (also known as P50) or about 100 ms after the onset of a visual stimulus. Functionally this component is typically interpreted as a neurophysiological indicator of preferential attention to sensory inputs. The auditory P1 appears earlier in time (shorter latency) over posterior scalp electrode sites but with larger amplitudes over frontal and/or central regions. It has been reported that P50 response was largest over the Cz electrode. The distribution is symmetrical over the two hemispheres except for the anterior temporal regions where larger amplitudes are noted over the left hemisphere. Overall, peak amplitude and latency appears to decrease with age to the point where the peak disappears.
Auditory P1 has been frequently associated with auditory inhibition in a sensory gating paradigm where paired clicks are presented at relatively short inter-stimulus intervals. The amplitude of the averaged ERP to the second of the paired clicks is typically reduced compared to the averaged response to the first click. The magnitude of this suppression is commonly interpreted as a neurophysiological index of sensory gating. Reduced suppression is frequently reported for schizophrenic patients. However in certain neuropsychiatric disorders, including mania and schizophrenia, peak amplitude to paired stimuli is reported to be approximately equal. P1 latency is frequently used clinically to diagnose neurodegenerative diseases, such as multiple sclerosis and Parkinson's disease.
It has been proposed that the P50 response is associated with the ascending reticular activating system (RAS) and its post-synaptic thalamic targets. The sources of P50 were subsequently and independently localized in the superior temporal gyrus using a MEG approach. Coregistered auditory evoked potentials (AEPs) and magnetic fields (AEFs) produced a resulting equivalent dipole model for the AEP consisting of one source in the auditory cortex of each hemisphere as well as a radially oriented medial frontal source.
Information regarding the visual P1 response differs from the auditory P1 literature in terms of the evoking stimulus, neurocognitive and neurophysiological mechanism, peak latency, scalp distribution, and neural sources. The visual P1 is typically recorded in a checkerboard-reversal task or similar light-flashes paradigms but can also be present for other visual stimuli (e.g., faces) and is largest over the occipital regions. A negative component may be present at the same latency over frontal and central regions. The amplitude of P1 generally varies with the amount of attention in Posner's attention cueing paradigm and in spatial selective attention experiments. It has been proposed that P1 reflects suppression of noise because the amplitude decreased for unattended locations and did not increase for attended stimuli. The P1 amplitude also increased when speed of response was emphasized, suggesting that P1 may also reflect the level of arousal.
Probable sources were identified using PET, BESA, and LORETA methods in ventral and lateral occipital regions, suggesting a striate or extrastriate (posterior fusiform gyrus) origin. A face identification paradigm reported similar sources as well as sources in posterior-parietal regions, suggesting the additional involvement of dorsal and ventral neural components.
The N1 component typically occurs approximately 100 ms after stimulus onset and is one of the most easily identified components regardless of the specific analysis approach employed. There is good convergence in findings based on analyses of PCA factor scores, baseline to peak amplitude, and baseline to peak latency.
Generally, N1 is assumed to reflect selective attention to basic stimulus characteristics, initial selection for later pattern recognition, and intentional discrimination processing. Latency and amplitude of the peak depend on the stimulus modality. Auditory stimuli elicit a larger N1 with shorter latency than visual stimuli.
For auditory stimuli, N1 has a maximum amplitude over frontocentral areas or the vertex. More recent studies differentiated it into three different components with maximum amplitudes over temporal areas (latency 75 ms and 130 ms) and over vertex (latency 100 ms). Based upon review of the three components of N1,it was proposed that the early temporal and vertex components reflect sensory and physical properties of the stimuli (e.g., intensity, location, timing in regards to other stimuli) while the later temporal component appears to be less specific in its response and reflects transient arousal. However, the majority of the studies reviewed in the present manuscript treated N1 as a single component occurring at 100 ms after stimulus onset with maximum amplitude at the vertex electrode.
The amplitude of the auditory N1 is enhanced by increased attention to stimuli and by increasing the inter-stimulus interval. The latter has been attributed to contributions of additional sources from frontal cortical areas. N1 appears most likely generated by sources in primary auditory cortex in the temporal lobe. MEG, BESA, and lesions studies consistently localize auditory N1 in superior temporal plane. However, several studies proposed additional sources in the frontal lobe that could be activated from the temporal lobe.
The visual N1 component is usually largest (maximum) over the occipital region or the inferior temporal regions. N1 amplitude is typically larger in discrimination tasks, but is reduced when stimuli are presented at short intervals. The N1 discrimination effect is attributed to enhanced processing of attended location and not due to arousal because the amplitudes were larger in a task that placed no emphasis on the speed of response. It is also not affected by inhibition as indicated by the lack of Go/No-Go response differences. Additionally, similar to the auditory N1,a visual N1 also occurred at 100 ms over the central midline sites and at 165 ms over the posterior sites. The researchers attributed the anterior N1 solely to response preparation processes because it could be eliminated by not requiring a motor response.
Using a combination of techniques (MEG, ERP, and MRI), the visual N1 sources were located in the inferior occipital lobe and the occipito-temporal junction. However, using the LORETA approach, additional sources of the visual N1 were identified in the inferior temporal lobe.
The P2, like the N1 and P1, has long been considered to be an “obligatory cortical potential” since it has low interindividual variability and high replicability. The P2 component has been identified in many different cognitive tasks including selective attention, stimulus change, feature detection processes, and short-term memory. P2 is sensitive to physical parameters of the stimuli, such as loudness. Participant differences, such as reading ability, can also change the P2 amplitude to auditory stimuli.
In the auditory modality, P2 often occurs together with N1, yet the two peaks can be dissociated. The distribution of the P2 is less localized than that of the N1 and has the highest amplitude over the central region. Also, the temporal peak of the P2 can occur over a broader latency range than the preceding peaks with latency ranging from 150-275 ms, and can be double-peaked. Similar to N1, P2 has been consistently identified by PCA factor scores, baseline to peak amplitude, and baseline to peak latency analysis procedures.
Generators for the auditory P2 are thought to be centered mainly in the primary and secondary auditory cortices. When using dipole source analysis, both the N1 and P2 elicited by auditory stimuli are often represented by two dipoles: one for the primary auditory cortex and one for the secondary auditory cortex. Using BESA and LORETA to identify dipole locations for the N1/P2 component, one in the superior temporal region was identified with a tangential orientation while the second was located in the temporal lobe with a radial orientation. These dipoles reflected the primary and secondary cortices respectively. However, it is difficult to differentiate the peak-specific locations because dipole source analysis is still relatively primitive, making it difficult to disentangle the individual effects of the neighboring structures.
In the visual domain, the amplitude of P2 increases with the complexity of the stimuli. Topographic distribution of the visually elicited P2 is characterized by a positive shift at the frontal sites around 150-200 ms after stimulus onset and a large negativity, approximately 200 ms following stimulus onset at the occipital sites. Using BESA dipole analysis, a symmetrical dipole pair localized in the inferior occipital (extrastriate) areas was reported. The findings suggested that both topographic distribution and dipole position varied slightly when attending vs. not attending to the visual images.
Very few studies have investigated the “basic” N2 peak; rather, it is considered to be a family of responses that differ based on features of the experiment, such as modality and stimuli presentation parameters. These components share some of their functional interpretation with mismatch negativity (MMN; see below) because both appear to indicate a detection of a deviation between a particular stimulus and the subject's expectation. However, unlike the MMN, in order for N2 to be present the subject must pay attention to the stimuli. In a study, participants viewed two stimuli; the first was expected to give information about the image that was to follow. When the following image did not match what was expected, they observed a larger N2 with frontal distribution, compared to when these expectations were met.
The N2 component has multiple psychological interpretations including orienting response, stimulus discrimination, and target selection, possibly reflecting task demands. Further, N2 is characterized by higher interindividual variation. Findings also show that the N2 is smaller in amplitude and shorter in latency for shorter interstimulus intervals.
The topographic distribution of the N2 depends on the sensory modality of the stimulus. Specifically, auditory stimuli elicit the highest N2 amplitudes at the vertex. Based on scalp current density analysis, it has been suggested that the N2 has bilateral sources in the supratemporal auditory cortex.
It has been reported the N2 amplitude reached its highest over the preoccipital region. While traces were reported of frontal activity, this frontal activity did not contribute appreciably to the visual N2 distribution. Further, N2 to visual stimuli varied based on the stimuli type, such as written words, pictures of objects, or human faces. Using intracranial electrodes placed directly on the cortex, it has been observed that letter-strings of recognizable nouns produced a N2 component at the fourth occipital gyrus near the occipitotemporal sulci. Pictures of complex objects, such as cars and butterflies, resulted in an N2 response over the inferior lingual gyrus medially and the middle occipital gyrus laterally. However, this effect was not present for the scrambled pictures. Face recognition tasks elicit an N2 over the fusiform gyrus and inferior temporal or occipital gyri just lateral to the occipito-temporal or inferior occipital sulci. The differential processing of human faces has led many researchers to investigate the visual processing of human faces (see N170 below). These differing distributions indicate that the N2 peak may reflect category-specific processing
The N2 is also associated with the Go/No-Go paradigm, in which the participant is asked to respond to some stimuli (Go trials), but inhibit the response to another class of stimuli (No-Go trials). The ERPs on No-Go trials are characterized by a large negative peak relative to the Go trials between 100 and 300 ms after stimulus onset. Given the nature of this task, it is often thought to be associated with response inhibition; it has been shown, though, that this response occurred both in relation to overt and covert responses, indicating that the N2 Go/No-Go effect cannot be completely attributed to motor responses. Instead, it appears to be present whenever responses must be interrupted.
The amplitude and polarity of the N2 inhibition response can change depending on the complexity of the task. In some instances, the Go/No-Go response has been reported as a positive peak. It has been suggested this pattern was due to large amplitude of the P300 in difficult tasks. Similarly, the effect is larger when subjects have less time to respond.
The N2 for the visual and auditory task is especially strong over the fronto-central electrodes when the Go response is withheld. This scalp distribution is different from that of the Error Related Negativity (ERN) that occurs approximately 125 ms after an incorrect response is made. It has been shown that the N2 response engages different processes than the error monitoring processes reflected in the ERN.
Using both ERP and fMRI, the involvement of the caudal and motor anterior cingulate cortices has been identified during both correctly and incorrectly inhibited responses. These sources differed from ERN responses that were related to caudal and rostral anterior cingulate cortices, providing additional support to the theory that the N2 reflects inhibitory responses that are distinct from the error-related negativity.
The N170 peak ranges in peak latency between 156 and 189 ms and is associated with visual processing of human faces. The topographic distribution of the N170 component for both familiar and unfamiliar faces is largest over the occipito-temporal regions. Its amplitude is significantly larger when viewing faces than when viewing other natural or human-made objects. Additionally, patients suffering from prosopagnosia do not show an N170 response to faces. It has been argued that the N170 is not specific to human faces but to expert object recognition, finding that dog experts showed an increased N170 to pictures of dogs but not birds, while bird experts showed the opposite effect.
Intracranial recordings of evoked potentials and fMRI studies all point to the fusiform gyrus as the possible neuroanatomical substrate of N170. However, source localization of the N170 using BESA identified the potential source in lateral occipitotemporal region outside the fusiform gyrus.
The MMN wave is a negative deflection that has a typical latency of 100-250 ms. The amplitude is largest at frontal and central electrode sites. MMN is elicited using an “oddball paradigm” where an occasional deviant stimulus is presented in a stream of more frequent standard stimuli. Because MMN paradigms require no attention to the stimuli, they have been widely used in developmental research and sleep studies.
In the auditory modality, the MMN can be evoked by any perceivable physical deviance from the standard stimulus, such as changes in tone duration, frequency, intensity, and interstimulus interval. It is thought to be an index of the early, preattentive sensory memory, most likely only echoic memory. Most often MMN is used to test the ability of the subject to discriminate linguistic stimuli (e.g., speech sounds with different voice onset time or place of articulation. Frequently, data are analyzed by subtracting the average ERP elicited by the standard stimuli from the average ERPs for the deviants. This subtracted component generally displays an onset latency as short as 50 ms and a peak latency of 100-200 ms.
Using MEG, significant differences have been found between dipoles produced by deviants that differed in intensity, frequency and duration. Dipoles for frequency and duration deviants were located significantly inferior in comparison to the source of intensity deviants and differed significantly from each other in the anterior-posterior direction. All dipoles were located within the temporal lobes. fMRI and ERP data were recorded simultaneously to an MMN task. Increased BOLD signal were found were in the right superior temporal gyrus and the right superior temporal plane.
Though MMN is associated with considerable high test-retest reliability, it is important to note that many features can influence the outcome of the MMN. While most researchers report a negative wave in association with the MMN, there have been reports of a positive wave around 200 ms corresponding to the MMN response. The exact reason for this difference has not been thoroughly investigated but may be due to differences in filter settings. Also, some reports indicate a substantially reduced MMN response in subjects not attending to the stimuli. Similarly, the probability of the deviant stimuli can influence the nature of the effect. Given the use of ERP averaging to remove noise from the data, researchers must maintain a balance between presenting enough deviant trials to obtain low-noise average responses, and not allowing the subject to habituate to the deviant, thus diminishing the effect. The habituation of adults, children, and guinea pigs were mapped for complex and simple stimuli using the MMN paradigm. It was found that as the number of exposures increased the size of the MMN response decreased (though not in a linear fashion), but that time for habituation varied as a function of the complexity of the stimuli.
The MMN for visual stimuli has been difficult to obtain, although there is some evidence that it can be captured with optical techniques. Source localization techniques suggest the involvement of the primary visual cortex and/or adjacent areas.
At this time, the P3 is the most extensively researched ERP component. A pronounced positivity occurs in response to an unexpected stimulus type approximately 300 ms after stimulus onset. Currently, the most typical paradigm for eliciting the P3 component, also known as P3b, is the oddball paradigm where a target stimulus is presented infrequently among more common distracter stimuli. However, the P3 could also be elicited in a single stimulus paradigm where a rare stimulus is presented randomly in time. For a P3 to be elicited, the subject must pay attention and respond to the stimuli (unlike the MMN paradigms) and the ratio of target to distracter stimuli must be low (the fewer targets the larger the peak). P3 amplitude is affected by attention, stimulus probability, and stimulus relevance as well as by the amount of processing resources available, such as in single vs. dual tasks, the quality of selection, and attention allocation. The length of the interstimulus interval could also affect the amplitude independently of stimulus probability with shorter intervals resulting in a larger P3. P3 latency was reported to vary with stimulus complexity, effectiveness of selection and sustained attention.
The visual P3 is larger and has a longer latency than the auditory P3. In a 3-stimulus oddball paradigm, a larger P3 was reported for target vs. nontarget auditory stimuli, while visual stimuli elicited a larger P3 than auditory stimuli. P3 was largest over parietal regions and midline. Auditory stimuli elicited shorter latency P3 over parietal regions and longer latency over central sites.
The functional interpretation of the classic P3 is diverse—some view it as an indicator of memory updating while others believe that it reflects a combination of processes that vary by task and situation, including more elaborate active stimulus discrimination and responses preparation. P3 latency is assumed to reflect the duration of stimulus evaluation. The P3 component has also attracted attention in clinical studies. Because P3 amplitude varies with the amount of attention paid to the stimuli, this component is widely studied in populations with attention deficits (e.g., ADHD) where it is interpreted to reflect information regarding various attentional functions. Further, P3 latency was reported to be related to cognitive abilities with shorter latencies associated with better performance.
Sources of the P3 are not clearly identified but at least some are expected to be in the medial temporal lobe, including the hippocampal region related to memory, parahippocampal gyrus, amygdala, or thalamus. Lesion data suggest that there may be multiple generators, including the temporo-parietal junction. The possible sources were investigated and reported that selecting only one region (e.g., hippocampus or thalamus) resulted in poor model fit, but combining the different locations produced a better model. Their findings are consistent with earlier observations using MEG analyses that located sources in the floor of Sylvian fissure (superior temporal gyrus) as well as deeper sources in the thalamus and/or hippocampus.
A variant of P3, known as P3a, appears to have a different scalp distribution with frontal maximum and slightly shorter latency for stimuli in visual vs. auditory, and somatosensory modalities. This frontal P3a occurs when a subject is not required to actively respond to the targets or when a novel stimulus is added to the standard 2-stimulus oddball paradigm.
Frontal P3a is assumed to reflect involuntary attention as well as inhibition. In Go/No-Go paradigms, P3a was larger in amplitude in No-Go than Go conditions (maximal at parietal sites for Go). Regarding its neural substrate, sources of P3a have been identified in the medial parietal lobe (early: 317 ms) and in the left superior prefrontal cortex (late: 651 ms) for Go trials; for the No-Go trials (365 ms) the sources originated in the left lateral orbitofrontal cortex. Underscoring the prefrontal cortex connection, P3a can be reduced by lesions to frontal cortex.
The N400 negative component occurs approximately 400 ms after stimulus onset and is usually associated with semantic comprehension in both visual and auditory sentence comprehension tasks. This phenomenon was first identified in a paradigm where words of a sentence were visually presented one after another at fixed intervals in a serial manner. The last word of the sentence was either congruous (“He took a sip from the water fountain”) or incongruous but syntactically appropriate (“He took a sip from the transmitter”) with the rest of the sentence. The incongruous words elicited a larger amplitude N400 response than the congruous words. Further, the amplitude of the N400 was correlated with the degree of incongruency of the sentence to the final word. It was found that the N400 effect only held true for semantic, but not syntactic deviations from expected endings. Evidence indicates that listeners use the information gained from the wider discourse when interpreting the appropriateness of a particular word. The N400 is also elicited in semantic word pairs, semantic priming tasks and matching semantic material to visual displays.
In both visual and auditory displays, the N400 is larger for anomalous endings than expected endings over the parietal and temporal regions of the right hemisphere. There are differences in the N400 based on the modality of the task. The peak of the N400 is earlier in the visual (475 ms.) than auditory (525 ms) modality but only over the temporal, anterior temporal and frontal sites. Further, the earliest peak in the visual modality was over the parietal and temporal sites, while in the auditory modality it was over parietal and occipital sites. Asymmetries (with activation in the left hemisphere occurring earlier than activation in the right) were only noted in the visual modality. The N400 does not appear to be specific to written words, because spoken words and pictures can elicit this response. The N400 response was also elicited by incongruent solutions to mathematical multiplication problems.
The amount of attention necessary to produce the N400 and the precise cognitive processes involved remain unclear. It has been reported that the N400 is more robust with when attention is required but can occur even when participants are not attending to the stimuli. However, it has been reported that in a dichotic listening task, the N400 was absent for material presented in the unattended ear. The amount of effortful semantic processing required is also unclear. It has been reported an N400 effect even in tasks that did not require semantic processing although it has been found no N400 when the attention was not directed to the meaning of the stimuli. One consistent finding is that N400 can be elicited by anomalies in language presented in various modalities, including auditory presentation. However, N400 did not occur when participants were presented with anomalies in music, which is believed to involve a structure similar to language.
The N400 is likely to arise from multiple generators that are functionally and spatially segregated. Recent work points to the parahippocampal anterior fusiform gyrus as the generator for this effect. MEG studies have pinpointed the lateral temporal region as the origin of the N400 response. Intracortical depth recordings using written words point to the medial temporal structures near the hippocampus and amygdala.
The P600 component has two distinct functional interpretations, one associated with memory processes and another associated with language. Although the two variants of the P600 have roughly similar topographies, they appear to have different brain sources.
It has been suggested that the P600 component, especially the variant associated with language processes, is a delayed variant of the P3 because these peaks have relatively similar scalp distributions and are both sensitive to probability manipulations. In opposition to this view, it has been reported evidence that the P3 and P600 have sufficiently different scalp topography, are differentially sensitive to manipulations of stimuli and task, and have additive effects when they are co-elicited.
The P600 positive deflection typically begins at 400 ms, continues for approximately 400-600 ms, and is maximal over left temporo-parietal regions. This P600 old/new effect often co-occurs in time with a frontal N400 effect present over left fronto-central areas starting at 300-500 ms post-stimulus and continuing to 1200 ms and beyond. It has been noted that during the learning phase of a free recall task larger N400 and P600 amplitudes were elicited by items that were later forgotten. However, the two components have different functional interpretations. P600 is assumed to reflect recognition for the stimuli while frontal N400 is associated with stimulus familiarity.
Numerous studies of recognition memory reported a larger P600 in response to ‘old’ stimuli (previously presented to the subject) compared to ‘new’ stimuli that were not experienced before while the opposite is true for frontal N400. The P600 old/new effect also occurs for items that are incorrectly judged as ‘new’. In addition, it is often larger for correctly recognized words than falsely recognized lures and can be affected by depth of processing, and the amount of retrieved episodic information. Further, the amplitude of the P600 peak increases with better memory performance. A number of experiments have demonstrated that P600 old/new effects could also occur in the absence of intentional retrieval. However, some have reported that intentional retrieval resulted in enhanced P600 old/new effects.
Although most of the P600 studies involve visually presented stimuli, some work has employed auditory stimuli. For example, it has been noted no difference in the size of the P600 when the words were studied in one modality but tested in another. Similarly, it has been reported the old/new P600 effect after training subjects on auditory stimuli and testing them when the same stimuli were presented visually. These findings suggest that the component is not modality specific.
Various techniques consistently identified several brain sources for the P600 old/new effect. Using intracranial ERP recordings during continuous recognition tasks, it has been noted that P600 responses in prefrontal regions and anterior temporal lobe structures. Further, it has been reported that a large amplitude P600 response in the anterior cingulate gyrus. Similar findings were obtained in studies employing PET and ERP methods. PET data indicated that rCBF in the left posterior hippocampus, left frontal and temporal cortex, and left anterior cingulate were greater during the recognition of deeply processed (sentence generation vs. alphabetic judgment) words. Event-related fMRI imaging has been utilized and found that during the study period, words subsequently given recalled versus familiar judgments were associated with increased activity in a posterior left prefrontal region. However, during the memory task, recalled words were associated with enhanced responses in anterior left prefrontal, left parietal, and posterior cingulate regions relative to familiar judgments.
It has been reported that syntactic anomalies elicited a small early negativity and a small later positivity rather than a N400 response. A decade later, two independent research teams identified a specific component, variously referred to as P600 or the Syntactic Positive Shift. This component consists of a slow positive shift, lasting up to 300 ms, that begins approximately 500 ms after word onset and is widely distributed across the scalp, with a posterior maxima.
The syntactic P600 is typically elicited by various syntactic or morphosyntactic violations, including violations of agreement, phrase structure, subcategorization frame, and subjacency. It has also been elicited by syntactically ambiguous sentences. This syntactic P600 was reported in studies using various languages, such as English, German, and Italian. Syntactic P600 is also thought to reflect additional grammatical processing performed in response to a parsing failure (Hagoort, et al., 1993; Osterhout, 1994; Friederici & Mecklinger, 1996). Although the P600 is usually elicited by means of visually presented written stimuli, it can also be elicited using naturally produced speech (Friederici, et al., 1993; Hagoort & Brown, 2000).
Investigation of the neuroanatomical sources of the syntactic P600 using rapid-presentation event-related fMRI methods identified greater activation in the superior parietal cortex and the precuneus and posterior cingulate on the medial surface in response to morphosyntactic violations compared to normal sentences (Kuperberg, et al., 2003). An ERP study of 14 aphasic patients with lesions in basal ganglia or in temporal-parietal areas noted that only the group with basal ganglia lesions failed to display a P600 effect in response auditory stimuli containing syntactic violations (Frisch, et al. 2003). However, both groups displayed a clear P300 effect for the P3b in response to an oddball paradigm. Such results suggest that the basal ganglia play a crucial role in the modulation of the syntactic P600.
From the review, it is evident that a notion of specific peaks reflecting specific cognitive processes is a long outmoded view. In the early years of electrophysiological research, equipment limitations made it very difficult or impossible to record and/or analyze more than a single peak or to record from more than a few electrode sites. This may have led investigators to conclude that the measured component was the sole indicator of the cognitive process in question. In the interim, decades of research and advances in technology have increasingly demonstrated that each of the ERP components can be elicited by multiple stimuli and paradigms that tap different cognitive processes. This view is in line with the common understanding of brain organization—the same structures may participate in different processes to varying degrees at different times.
Further, it clear that peak characteristics can be affected by the procedures used to record ERPs. Differences in number of trials or length of intertrial intervals, variations in stimulus intensity or modality can contribute to inconsistent outcomes. Therefore, to increase the chance of successful replication, investigators must routinely report (and review) such details.
There are potential problems of interpretation, directly linking the scalp distribution of an ERP component with brain structures located below the specific electrodes. Brain sources of the components are often located not immediately below the electrode that recorded the maximum amplitude. In some cases, the sources are not even in the same hemisphere. Development of carbon electrodes as well as brain source analysis software now allows researchers to co-register EPRs with fMRI methods to map ERP components onto brain structures and to model potential sources of the observed activity across procedures.
Most scientific studies to-date have used discrete statistical methods to evaluate the ability of specific ERP tests to discriminate various neurological attributes between test subjects. This involves identifying the relevant peaks and comparing the amplitudes and latencies between different test subjects. Statistical distributions are calculated and cluster analyses are performed to ascertain if the peak amplitude and latency values correlate in some way with various neurological attributes, abilities, and/or disabilities. Discriminate analyses as described above have used ERPs to evaluate IQ, reading ability, language skills, Alzheimer's, attention deficit hyperactivity disorder (ADHD), and many other cognitive factors.
The tremendous clinical potential of the ERP method has been well recognized although because of certain system and methodological limitations this potential has only rarely been realized. These problems generally fall into four areas: (1) complicated, difficult to use, and incompatible hardware systems; (2) lack of standardized testing protocols including: stimuli, stimuli sequencing and timing, signal processing, testing environment; (3) analytical methods which do not provide statistically powerful or reproducible results, due to signal processing limitations, non-algorithmic peak detection methods, and disregard for information in the ERPs other than the peaks, and (4) requirement to perform large population studies in order to discriminate small neurological variations.
In addition to these shortcoming, and others noted in the research, it would be desirable that a system for performing ERP tests and analyzing the resulting data be developed that would be suitable for the clinical environment. The equipment expense, level of training required to perform these brainwaves studies, and unreliable results renders them impractical for widespread screening and diagnostic use.
Consequently, a significant need exists for an ERP testing system and method that is suitable for widespread clinical use.